Ray.io is a widely adopted open-source framework for distributed computing that offers scalable and efficient execution of machine learning tasks. IN this session we will cover the details of how to adapt OSS to meet more stringent Enterprise-ready requirements such as security and networking. We will examine a set of ten best-practices to adapt OSS for the Enterprise and demonstrate how to further enhance Ray.io's suitability for Enterprise-level computing. Our discussion will encompass topics ranging from bolstering security features to improving scalability and reliability through integration with GKE. Our goal is to illustrate how Ray.io can be optimized to meet the demanding requirements of Enterprise environments, resulting in a more robust and scalable solutions esp focused on AI/ML workloads. We invite you to join us as we explore the exciting possibilities of Ray.io in the realm of Enterprise computing.
Dr. Ali Arsanjani is the Director of Cloud Partner Engineering at Google Cloud, and Head of AI Center of Excellence, where he leads the development of strategic co-innovation partnerships in the fields of Generative AI, Data/Analytics & Predictive AI/ML. His team specializes in co-innovation with ISV and GSI partners as they run, integrate and build on GCP across the ML Lifecycle. Ali also works closely with product management to shape the direction of Google's AI and analytics offerings from a cloud perspective.
In addition to his role at Google Cloud, Ali is an Adjunct Professor at San Jose State University
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